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ThinkM, PECHS Block 2, Karachi (2026)

01/09/2026

Finding a new electronic material can mean searching through an almost limitless number of possible chemical combinations. Researchers at Seoul National University have used artificial intelligence to make that search more manageable, pulling together data scattered across hundreds of scientific papers and using it to identify lead-free dielectric materials that remain stable at high temperatures.

The Seoul National University College of Engineering research team was led by Professor Ho Won Jang of the Department of Materials Science and Engineering. The researchers combined information extracted from published studies with physics-informed machine learning to design new lead-free dielectric compositions. Integrated M.S./Ph.D. student Kwanwoo Song was first author and led the overall project, with integrated M.S./Ph.D. student Youngmin Kim and postdoctoral researcher Jaehyun Kim also contributing.

Dielectrics are insulating materials that block the direct flow of electricity while storing electrical charge. They are essential to multilayer ceramic capacitors (MLCCs) found in smartphones, electric vehicles, and other electronics. A higher dielectric constant allows a component of the same size to store more electrical energy, but useful materials must also preserve that performance as temperatures rise.

To search for promising compositions, the researchers combined multimodal literature mining, which extracts information from text, tables, and graphs, with physics-informed machine learning. Their inverse design strategy began with desired performance targets and worked backward to identify compositions likely to meet them.

The researchers assembled 1,202 records of dielectric properties from 448 scientific papers, then screened a virtual chemical space containing approximately 150 million possible compositions. That process reduced the field to 37 candidates. Two were synthesized and tested experimentally, and both showed high dielectric constants along with strong stability at elevated temperatures.

Demand for heat-resistant dielectric materials is growing as technologies including electric vehicles, power electronics, and aerospace equipment increasingly operate at elevated temperatures. Relaxor ferroelectrics are particularly promising because their electrical response changes relatively gradually with temperature, potentially combining a high dielectric constant with performance across a wide temperature range.

Even among lead-free materials, however, the enormous number of possible elements and mixing ratios makes conventional trial-and-error searches expensive and slow. Another obstacle is the data itself. Useful measurements are scattered among the text, tables, and figures of numerous studies, while temperature, frequency, sample characteristics, and other experimental conditions differ across publications. Those inconsistencies make published information difficult to use directly for machine learning.

AI narrowed 150 million compositions to 37
The researchers addressed this problem by building a machine learning framework that organizes information from separate publications into one consistent dataset while using physical constraints to exclude compositions unlikely to exist.

Large language models were used to extract compositions and processing conditions from the text and tables of scientific papers. Graphs were converted into numerical data so the researchers could recover temperature-dependent dielectric properties.

Together, those sources produced 1,202 records containing composition, processing conditions, and dielectric properties from 448 papers. The researchers added 22 physical descriptors, including information related to elemental composition and microstructure, to make data from different publications more comparable.

25/08/2026

Diamond is not only a gemstone. This exceptionally hard form of carbon is also used in the tiny capsules that surround fuel in inertial confinement fusion experiments, and researchers think carbon may form diamonds that fall through the interiors of ice giants such as Neptune and Uranus.

Both environments expose diamond to immense pressure, yet experiments and computer simulations have long produced conflicting descriptions of what happens to the material under such extreme conditions.

Researchers at Lawrence Livermore National Laboratory (LLNL) have now measured diamond as it melts at pressures reaching three times those found at Earth’s core. Their findings were published in Nature Physics.

“We were able to take tiny diamond samples and shock compress them to temperatures hotter than the surface of the sun and to pressures higher than the center of Neptune and Uranus—and still measure atomic structure, temperature, density, and optical reflectivity,” said author and LLNL scientist Marius Millot.

The measurements resolve two persistent disagreements in the field and bring experimental results into close alignment with simulations based on quantum mechanics. The findings could have consequences for inertial confinement fusion, where predictions suggest they may enable up to three times greater energy gain, as well as for models describing the interiors of planets under extreme pressure.

A decades-old mismatch is resolved
LLNL researchers have spent decades investigating diamond under extreme conditions. About 20 years ago, lab scientist Jon Eggert and his colleagues carried out pioneering high-pressure melting experiments and found that diamond became denser when it melted.

“While this is rather unusual among most materials, we all know an example of such behavior,” said LLNL scientist Marius Millot. “Liquid water is denser than ice, which makes ice cubes float. Jon’s finding means that diamond would float in liquid carbon at high pressures.”

But those influential experiments also left behind a major puzzle. The experimentally measured melting temperature differed from theoretical predictions by roughly 20%.

“No matter what the theorists did—even with the most advanced computer simulation techniques—they could not reproduce the experiments,” said Millot.

Another unanswered question emerged from experiments at Sandia National Laboratories. Researchers there used the powerful magnetic fields of the Z machine to shock compress small diamond samples and detected signals suggesting that diamond might transform into another crystalline form before melting. Simulations supported that possibility, but the atomic arrangement had never been measured directly.

X-rays capture diamond as it melts
To resolve both problems, LLNL researchers performed laser-driven dynamic compression experiments at the University of Rochester’s Laboratory for Laser Energetics (LLE). At the Omega Laser Facility, lasers vaporized the outer surface of a tiny diamond sample, generating a powerful shockwave that compressed the material inside.

Obtaining precise measurements during this process was particularly difficult because the extreme pressure states lasted only about one billionth of a second. Within that brief interval, the researchers needed to collect multiple measurements, including X-ray diffraction, which reveals how atoms are arranged.

“This was the first time that shock-compressed diamond was probed with X-ray diffraction all the way up to melting,” said Millot. “These measurements are extremely difficult because carbon is a small and lightweight atom. It scatters very few X-rays, so the signal we needed to measure was quite faint.”

Improved diagnostic instruments developed and maintained by the LLE team produced a new measurement of diamond’s melting temperature. This time, the experimental value agreed almost perfectly with simulations, resolving the discrepancy that had persisted for about two decades.

“While it was frustrating to discover that our original temperature measurements were off by more than 1,000 degrees, it is exciting to see such a dramatic improvement in data quality with our new diagnostics,” said Eggert. “Even better, our original inference of melting has now been confirmed directly with X-ray diffraction.”

The experiment also produced a different result from the possible phase transition previously indicated at Sandia. Under a single shock, the carbon retained its diamond structure until it melted, with no intermediate crystalline phase detected.

“We think that is because the sample does not have time to change when it only experiences a single shock. It remains ‘trapped’ in the diamond structure,” said Millot.

That distinction may matter for future high-energy density experiments and simulations because it indicates that a material’s response depends not only on its pressure and temperature but also on the way the shock is delivered.

Slower shocks could boost fusion yield
The agreement between theory and experiment may have direct consequences for fusion experiments.

In inertial confinement fusion, powerful lasers generate shocks that force a tiny diamond capsule inward. The resulting implosion compresses the fusion fuel enclosed within it until the fuel reaches the extreme pressures and temperatures required for fusion.

During the first shock, the diamond needs to melt into a smooth, uniform fluid. Irregularities in the implosion can otherwise interfere with the fusion reaction. To ensure that melting occurs, scientists at LLNL’s National Ignition Facility (NIF) generally begin with a relatively strong shock.

“Our work indicates that we could use slightly slower initial shocks and still achieve full melting of the diamond in our NIF implosions,” said Millot. “This is exciting because such a slower shock would make the fusion fuel more compressible. That in turn increases the maximum energy yield we could obtain with the same laser energy.”

If other sources of performance loss can be controlled, predictions indicate that using these slower shocks could triple the energy gain.

The findings reach inside ice giants
The new measurements also provide useful information for scientists trying to understand Neptune and Uranus. Because the deep interiors of these ice giants cannot be observed directly, planetary researchers rely on experiments and models to determine what may occur far beneath their surfaces. Some studies suggest that carbon can crystallize at depth and fall through the planets as “diamond rain.” Since the latest experiments reached pressures greater than those expected inside ice giants, the revised melting data can help support more realistic models of their formation and evolution.

LLNL researchers next plan to use the capabilities of NIF to investigate diamond under even more extreme conditions that are difficult to reproduce elsewhere. They hope to determine how diamond capsules behave during later stages of an implosion and establish how long the diamond structure can remain stable when subjected to a sequence of multiple shock waves.

Reference: “Diamond melting in shock compression experiments at 1 TPa pressures” by Marius Millot, Federica Coppari, Amy Lazicki, Yong-Jae Kim, Otto L. Landen, Vladimir A. Smalyuk, Peter M. Celliers and Jon H. Eggert, 13 August 2026, Nature Physics.

14/08/2026

Sohni Dharti Allah Rakhay! 🇵🇰 💚

Our land is beautiful from every angle. This 14th of August, as we celebrate our nation's independence, ThinkM is proud to survey, inspect, and help build the future of this great country. We are committed to taking Pakistan's infrastructure to new heights with cutting-edge UAV technology and precision.

Happy Independence Day to all! ✨

31/07/2026

Free electrons offer chemists a new way to control reactions that conventional electron transfer rules would normally prevent.

Inside a reaction flask, an electron can determine which molecules combine and which remain unchanged. Chemists rely on this control to build complex compounds used in lifesaving drugs, advanced materials, and laboratory models of biological systems. One of their most useful tools is single-electron transfer, which can activate molecules that would otherwise resist reacting and allow them to join together.

For decades, however, a basic rule has restricted which reactions chemists can design. When two molecules compete to receive an electron, the electron ordinarily moves to the molecule that is easier to reduce. Researchers led by chemists at the University of Wisconsin–Madison, working with colleagues at Colorado State University and the University of Colorado Boulder, have now developed a different approach. Reported in Nature, the strategy overcomes a persistent problem in electron transfer selectivity and may make previously inaccessible coupling reactions possible.

Free electrons bypass the usual preference
“Our catalyst works a bit differently because it actually just ejects the electron directly into solvent,” says Zachary Wickens, a professor in the UW–Madison Department of Chemistry who led the work. “This gives you, more or less, the strongest reductant and the most aggressive source of electrons you could possibly have since a free electron would rather be in basically any molecule than just on its own in solution.”

The analyses showed that the decisive selection does not occur when the electron first enters a molecule. Instead, it emerges during the steps that follow.

“Our calculations reveal how the decisive selectivity emerges after electron transfer has already occurred,” says Paton. “We found that the desired reactant can escape reversal and continue toward product, while the partner that is easier to reduce is effectively recycled back to its starting material. This explains how the reaction can succeed despite the usual thermodynamic preference.”

The molecule needed for the desired reaction can continue toward the final product, while the competing molecule that more readily accepts an electron reverses course and returns to its original form. This sequence allows chemists to overcome the usual preference imposed by thermodynamics.

A broader framework for redox chemistry
Wickens and his colleagues have spent the past five years developing the catalyst family that made this alternative approach to selectivity possible. Rather than introducing only one additional laboratory technique, the work provides a broader principle for planning reactions involving oxidation and reduction.

According to Wickens, “This is not just another synthetic method; it’s a new way to design redox reactions.”

Reference: “Selectivity Emerges from Indiscriminate Photoreduction” by Joseph M. Edgecomb, Arindam Sau, Niket Manoj, Matthew D. Resmini, Alissia F. Meyer, Robert S. Paton, Niels H. Damrauer and Zachary K. Wickens, 15 July 2026, Nature.

18/07/2026

One of the largest obstacles limiting the next generation of computer chips may finally have a solution.

The shrinking of computer chips has exposed a stubborn problem: even when a semiconductor can carry electricity efficiently, getting that electricity into the material can waste power and slow the device down.

Researchers in South Korea have now demonstrated a possible way around this obstacle. Their design allows electrical current to move smoothly from a conductive region into a semiconducting region without crossing the conventional junction between two separate materials. The team also directly mapped the movement of charges at the nanometer scale, providing experimental evidence that the new interface does not disrupt the current.

The advance could support the development of smaller and more energy-efficient electronics, including AI processors, low-power devices, and future logic chips.

The research was led by Professor Seungbum Hong of KAIST’s Department of Materials Science and Engineering, in collaboration with Professor Kibum Kang at KAIST and Professor Sung Beom Cho’s team at Sungkyunkwan University.

Ultralow Barrier Charge Flow in a Monolithic 2D Junction
Infographic about the study. Credit: KAIST

Why Contact Resistance Holds Back Smaller Chips
Modern transistors depend on metal electrodes to deliver electricity into a semiconductor. However, the boundary where those materials meet can resist the movement of electrical charges. This contact resistance consumes energy, produces heat, and limits how much performance engineers can gain by making transistors smaller.

The problem is particularly important for two-dimensional semiconductors. These materials can be only one or a few atomic layers thick, making them attractive for electronics that may eventually need to operate at dimensions beyond the practical limits of conventional silicon. Yet their extreme thinness also makes it difficult to create efficient electrical contacts without damaging or altering the semiconductor.

Instead of placing a separate metal electrode on top of the semiconductor, the researchers created conductive and semiconducting regions inside one continuous sheet of platinum diselenide (PtSe₂).

15/07/2026

The global transition to clean energy depends on a handful of materials that most people never see. Rare earth magnets power everything from electric vehicles and offshore wind turbines to smartphones and advanced defense systems, yet the supply chain behind them is concentrated, environmentally intensive, and increasingly vulnerable to geopolitical tensions.

That tension is what Martin Sahlberg, Professor of Materials Chemistry at Uppsala University, wants to change.

“It’s a geopolitical problem,” he says.

China dominates rare earth processing and magnet production, giving it enormous influence over materials needed for clean energy, electronics, defense systems, and advanced manufacturing. Recent export controls have shown how quickly that dependence can turn into a supply risk.

Why Rare Earth Mining Is So Difficult
Rare earths are also difficult to produce cleanly. Separating them from rock often requires harsh chemicals, and radioactive elements can occur in the same deposits.

“It’s rather a dirty business today,” says Martin Sahlberg.

Monazite From Djupedal
Monazite is a fairly common mineral that contains rare earth elements. Here, monazite from Djupedal, Västervik. Credit: Julia Sordyl.
The irony is that rare earth elements are not always rare. The real challenge is finding them in deposits rich enough to mine, then separating them without creating a new environmental problem.

Sweden may have an advantage. Deposits have been identified in places such as Kiruna, Bergslagen, and Norra Kärr outside Gränna. LKAB has described the Kiruna area as home to Europe’s largest known rare earth deposit, with more than 1.3 million tonnes (about 1.4 million U.S. tons) of rare earth oxides reported at Per Geijer.

Sweden’s Plan for Cleaner Rare Earth Magnets
“In Sweden our possibilities for extracting REE, even when compared internationally, are relatively good,” Sahlberg says.

Martin Sahlberg
Martin Sahlberg, Professor of Materials Chemistry, Uppsala University. Credit: Mikael Wallerstedt.
His research focuses on a different way to think about rare earth mining. Instead of digging for one target metal and treating the rest as waste, the team wants to map the full chemical mix inside Swedish deposits and design magnets around what is actually available there.

“It’s a bit like the TV show What’s in Your Fridge,” says Martin Sahlberg.

“Historically, we have mined for a specific metal, iron, copper, or maybe gold. We’re taking a broader approach here to find out what elements there are in the deposits and in what proportions. We make an inventory of ‘what’s in the fridge’ so that we can use all these elements in the most efficient way possible. We’re creating new ‘magnet recipes’ based on the elements we have available,” he explains.

That could make rare earth production less wasteful from the start. If magnets can be designed around local mineral chemistry, Sweden may need fewer intensive purification steps and could reduce the environmental footprint of both refining and manufacturing.

Building a Sustainable Rare Earth Supply Chain
“Today, China basically has a world monopoly, but we not only have deposits but also good access to water and relatively cheap energy. There is also an interest in leading the green transition here in Sweden,” he continues.

The project brings together theoretical physicists, geologists, and materials engineers to trace a cleaner route from raw rock to finished magnet. Sahlberg calls it application-inspired basic research.

22/06/2026

Researchers in Sweden have developed a machine-learning approach that embeds the laws of physics directly into neural networks.

A new study from Chalmers University of Technology in Sweden shows that machine learning can become far more efficient when it starts with a built-in understanding of the laws of physics. Researchers found that giving an AI system this foundational knowledge dramatically reduced the time needed to develop advanced optical components used in technologies ranging from quantum computers to camera and eyeglass lenses.

“When we fed the super-brain information about the laws of physics, it immediately got much smarter. Our calculations now take one tenth of the time previously required,” said Philippe Tassin, a professor in the Department of Physics and Astronomy at Chalmers University of Technology.

Tassin’s team works in nanophotonics, a field focused on controlling light at extremely small scales. When light interacts with structures smaller than its wavelength, it can behave very differently than it does on larger scales. However, natural optical materials have limits that restrict how light can be manipulated. To overcome those constraints, the researchers use computer simulations to design artificial optical materials.

These engineered materials could lead to lighter, thinner, and more effective camera and eyeglass lenses. The research may also support future quantum computing technologies. Working with scientists from Chalmers’ Department of Microtechnology and Nanoscience, where Sweden’s first large-scale quantum computer is under development, the team is exploring nanostructured materials that can precisely control the movement of light.

One potential application involves transmitting information between quantum computers, or across longer distances, using optical frequencies and mechanically compliant photonic crystals. These specially designed crystals can reflect light with extremely high efficiency.

The researchers rely entirely on supercomputer simulations, using machine learning and neural networks to analyze how different materials behave. These tools help identify material properties and guide the design process.

“I know electromagnetism’s equations inside out and I teach them, but I still can’t draw all the conclusions that the neural network can. The physics is so complex that I don’t understand the properties of a material just by looking at it – but the computer does,” says Philippe Tassin.

Time-consuming to feed data into neural networks
Training neural networks for these simulations has traditionally required enormous amounts of data. Creating a single data point can take anywhere from ten minutes to an hour, and researchers may need as many as 40,000 simulations.

13/06/2026

Researchers are replacing rigid silicon-based AI hardware with stretchable, neuromorphic electronics that mimic how the brain processes information, opening new possibilities for long-term human-machine integration.

Modern artificial intelligence can outperform humans in tasks ranging from image recognition to medical data analysis, but there is one environment where today’s hardware still struggles: the human body.

The problem is surprisingly simple. Human tissues are soft, flexible, and constantly moving. Conventional electronics are not. Even the most advanced silicon chips remain rigid, making long-term integration with organs, muscles, and skin extremely difficult. Devices attached to a beating heart, expanding lungs, or bending joints can irritate tissue, lose contact, and eventually fail.

Researchers are now pursuing a radically different approach. Instead of forcing the body to adapt to electronics, they are redesigning electronics to behave more like the body itself.

A review published in the International Journal of Extreme Manufacturing highlights the rise of soft neuromorphic electronics, a new class of devices that combine sensing, memory, and computing in materials that can stretch, bend, and conform to living tissue. The technology draws inspiration from the brain, not only in how it processes information but also in how it physically interacts with its environment.

Electronics Inspired by the Brain
Unlike traditional circuits that rely exclusively on electrons moving through metal pathways, these systems use soft materials such as flexible polymers and gel-like ionogels that transport both electrons and ions.

This mechanism, known as organic mixed ionic-electronic conduction, more closely resembles the electrochemical signaling used by the nervous system. The active materials can absorb and release ions from their surroundings, continuously altering their internal electrical state.

As a result, a single soft transistor can mimic synaptic plasticity, the biological process that allows brain cells to strengthen or weaken connections over time. In effect, the hardware itself can exhibit behaviors similar to the learning mechanisms found in the brain.

Stretchable and Energy Efficient
Recent advances in materials science have pushed these devices to impressive levels of flexibility. Some components can stretch to 140% of their original length, exceeding the natural stretchability of human skin and allowing them to function across highly mobile areas of the body.

The devices also operate with extremely low power requirements. By relying on efficient electrochemical processes rather than large electrical currents, they can perform complex tasks, including heart rhythm classification, at voltages below 0.5 volts.

Such low operating voltages help minimize heat generation and electrical stress, two critical considerations for electronics designed to remain in continuous contact with living tissue.

The technology could also reshape wearable device manufacturing. Rather than mounting rigid sensors onto flexible substrates, engineers could print integrated soft computing networks that combine sensing, memory, and processing within a single stretchable material. This approach could enable electronic skin and soft robotic limbs capable of interpreting touch and movement locally instead of constantly sending data to an external computer.

Moving Beyond the Laboratory
Despite the progress, significant technical hurdles remain before soft neuromorphic electronics can be used clinically.

One of the biggest challenges is memory retention. Many current soft memory devices lose stored information quickly after a signal ends, limiting their usefulness for long-term data storage.

To address this issue, researchers are focusing on island-bridge architectures. These designs place permanent memory components on tiny rigid islands that are protected from mechanical strain, while highly stretchable coiled connections link the components together.

Researchers believe that combining these architectures with chemically stable, non-toxic materials could provide a practical path toward durable neuromorphic devices capable of long-term integration with the human body.

06/06/2026

Hawaii researchers are testing whether plastic waste and abandoned fishing nets can be safely reused in asphalt roads.

Hawaii is struggling with plastic waste. Recycling is difficult and expensive for the island state, especially when the waste includes marine debris that remains in the surrounding ocean waters. Researchers in Hawaii are testing a way to turn discarded fishing nets and household plastic trash into asphalt roads. Early trials suggest these materials could give some of the islands’ waste a practical local use at the end of its life.

Jeremy Axworthy, a researcher at the Center for Marine Debris Research (CMDR) at Hawaiʻi Pacific University, presented the team’s results at the spring meeting of the American Chemical Society (ACS).

“This work investigates whether it’s responsible to use recycled plastics in Hawaii’s roads,” shares Axworthy. “By reusing plastic waste that is already in Hawaii, we can reduce the environmental and economic impacts of transporting waste plastics from the islands, incinerating it or dumping it in Hawaii’s overflowing landfills.”

Road asphalt offers a local outlet
Since 2020, most roads in Hawaii have been paved with polymer-modified asphalt (PMA) to make pavement stronger and longer-lasting. Compared with regular asphalt, PMA is more flexible and better able to resist cracking, rutting, and water damage.

Those qualities are especially useful in Hawaii’s tropical climate. PMA is produced by melting styrene-butadiene-styrene (SBS; a type of copolymer) pellets into a sticky asphalt binder made from petroleum. The binder is then mixed with hot aggregates (rocks and sand) inside a rotating drum so it fully coats the material.

The question was whether waste plastic could replace or supplement some of that material in road pavement as a more useful disposal route. The Hawaii Department of Transportation (HDOT) wanted to know how asphalt made with recycled plastics would perform, and whether it might release microplastics or related chemicals into the environment. To investigate, HDOT contacted environmental chemist Jennifer Lynch, director of CMDR and lead of the research team.

Fishing nets became test material
HDOT made two requests of Lynch’s team. First, the department needed derelict fishing nets collected from Hawaii’s marine environment to use in recycled plastic modified asphalt. “Foreign plastic derelict fishing gear is the largest contributor of Hawaii’s marine debris problem,” shares Lynch. “To date, CMDR’s Bounty Project, which pays a financial reward to licensed commercial fishers for marine debris removal, has removed 84 tons of large, derelict fishing gear from the Pacific Ocean.”

Rachel Nakamoto, Simon Williams, Cara Megill and Cate Wardinski
Researchers collect road dust samples from a section of road paved with recycled plastic-reinforced asphalt. Pictured left to right: Rachel Nakamoto, Simon Williams, Cara Megill and Cate Wardinski. Credit: Marquesa Calderon

Second, HDOT asked the researchers to test whether pavement made with plastic waste shed more microplastics than standard pavement modified with SBS. “CMDR’s laboratory is equipped with state-of-the-art chemical instrumentation for quantifying and characterizing microplastics in environmental samples,” explains Lynch. “This capability is incredibly unique and impactful, especially when coupled to our marine debris-removal project and our mission to recycle the debris into long-term, locally necessary infrastructure products.”

Road dust tested the risk
After a company based in the United States converted the waste into materials suitable for asphalt, HDOT moved the experimental mixes onto real streets. A local paving company installed sections of a residential road on Oahu using asphalt that contained standard SBS, repurposed polyethylene from Honolulu recycling bins, and polyethylene from fishing nets. After roughly 11 months of normal traffic, Lynch’s team collected road dust from each pavement section to look for microplastic shedding that could affect nearby soil.

The researchers used a process that separates polymers from other road dust materials, including microplastics, larger plastic fragments, and tire rubber. With pyrolysis gas chromatography mass spectrometry (Py-GC-MS), they traced the polymers back to their sources: styrene and butadiene from standard PMA, polyethylene from pavement made with plastic waste and fishing nets, and isoprene and butadiene rubber from tires.

Early results showed that pavement made with recycled polyethylene did not shed more polymers than the control pavement made with SBS. Lynch’s team found the same pattern in mechanical performance tests using pavement samples and in simulated stormwater collected from the experimental road sections.

Microplastic-sized particles were found, but very few were identified as polyethylene, regardless of which pavement type was tested. The likely reason is that the polymers are melted into the asphalt binder, so fragments that break away are not pure plastic. They contain a mixture of rock, binder, and melted polymer chains.

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